A variety of medical imaging modalities, for example computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound, have become standard techniques in obtaining medical imaging data representative of a patient or other subject for diagnostic or other purposes. Medical imaging data can be in a variety of forms and can include any suitable data obtained from measurements by a medical imaging modality and/or any suitable data representative of one or more anatomical features. Medical imaging data may comprise any data that can be rendered, or otherwise processed, to obtain an image of at least part of a patient or other medical subject and/or any data that can be rendered, or otherwise processed, to obtain an image of one or more anatomical features. Volumetric medical imaging data may, for example, be in the form of an array of voxels. Such arrays of voxels may for example be representative of intensity, absorption or other parameter as a function of three-dimensional position, and may for example be obtained by suitable processing of measurement signals obtained by a medical imaging modality.
A wide variety of image data processing techniques have been developed to support known imaging modalities. For example, a variety of techniques have been developed to segment automatically or semi-automatically medical imaging data obtained using such modalities to identify regions of the data that represent different anatomical or other features.
Many such known segmentation techniques are based on intensity values of voxels in the imaging data sets. For example, in the case of CT techniques, a CT data set usually comprises a three-dimensional array of voxels each representing a respective position in a three dimensional volume that includes the patient or other subject. Each voxel usually has an intensity value (for example in Hounsfield units) that represents the attenuation of X-rays at the corresponding position in the volume, as determined from the CT measurements. In a simple case, a segmentation can be performed based solely on a thresholding of the voxel intensity values. For example, all voxels having intensity values in a particular range may be considered to represent bone.
Many more complex segmentation procedures have been developed based on intensity values, in which for example one or more of pattern recognition, fitting processes, morphological processes, comparison to atlases or other references, or other processes can be used to segment the medical imaging data.
It is also known to obtain multiple medical imaging data sets on the same patient or other subject by performing measurements at different times, under different conditions or using different modalities. Such multiple medical imaging data sets often have different co-ordinate systems such that the same anatomical feature of the subject will appear at positions having different co-ordinates in the different medical imaging data sets (for example, in a simple case, due to the patient or other subject having a different relative position within the scanner when the different imaging data sets were obtained).
It is known to register different medical imaging data sets, for example different medical imaging data sets for the same patient or other subject obtained at different times, to obtain registration data that comprises or represents a transformation of co-ordinates for one or both of the medical imaging data sets. By transforming the co-ordinates of one or both medical imaging data sets it can be provided that the medical imaging data sets are aligned such that the same anatomical features from the medical imaging data sets appear at substantially the same, or corresponding, positions in a common co-ordinate system.
It is known to perform registrations manually or automatically using known analysis techniques. Different types of registration may be used, for example rigid, affine, or non-rigid.
A rigid registration in this context may comprise a registration in which the co-ordinates of data points in one data set are subject to rotation and translation in order to register the data set to another data set. An affine registration in this context may comprise a registration in which the coordinates of data points in one dataset are subject to rotation, translation, scaling and/or shearing in order to register the dataset to another dataset. Thus, a rigid registration may be considered to be a particular type of affine registration.
Non-rigid registrations can provide different displacements for each voxel of the data set to be registered and can, for example, use non-linear transformations, in which the coordinates of data points in one dataset are subject to flexible deformations in order to register the data set to another data set. Non-linear transformations may in some cases be defined using vector fields such as warp fields, or other fields or functions, defining an individual displacement for each voxel in a three-dimensional data set.
It is known to use multiple registered medical imaging data sets in performing segmentation procedures. For example, in segmenting or imaging the vasculature it is known to introduce contrast agent into blood vessels of a patient, to obtain CT data of the patient when no contrast agent is present in a region of interest, and to obtain further CT data when the contrast agent is present in the region of interest. By registering the data sets, and subtracting or performing other processes on the registered data sets, the blood vessels can be displayed or identified based on the variation of intensities between the data sets due to the presence of contrast agent.
The accurate segmentation of some anatomical features in medical image data sets can be difficult, for example if they are close to other similar anatomical features or if they have intensity values that are close to intensity values of other anatomical features. Features that are small or have complex shapes can also sometimes be difficult to segment accurately.
It is known to perform CT imaging measurements of the lungs of a patient or other subject. Automatic analysis and effective visualization of the lungs can be dependent on the quality of segmentation and classification of the lungs. Segmentation of lung features, for example pulmonary fissures that separate the different lung lobes, can be difficult as in CT scans the pulmonary fissures can be indistinct and hard to track. Similarly, in some cases it can be challenging to distinguish between a chest wall and lung due to their close proximity.